Autonomous System for Tumor Resection (ASTR)-Dual-Arm Robotic Midline Partial Glossectomy

Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treat- ment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This letter presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creat- ing and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy work- flow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The av- eragesurfaceanddepthresectionerrorsmeasure0.73 ± 0.60mm and 1.89 ± 0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist. Manuscript received 18 September 2023; accepted 20 November 2023. Date of publication 12 December 2023; date of current version 26 December 2023. This letter was recommended for publication by Associate Editor L. Mattos and Editor J. Burgner-Kahrs upon evaluation of the reviewers’ comments. This work was supported in part by the National Institutes of Health under Grants 1R01EB020610 and R21EB024707, in part by the National Science Foundation’s Foundational Research in Robotics CAREER Program under Grant 2144348, and in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research. (Corresponding author: Jiawei Ge.) Jiawei Ge, Michael Kam, Justin D. Opfermann, and Axel Krieger are with the Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA (e-mail: jge9@jhu.edu; mkam2@jhu.edu; jopferm1@jhu.edu; axel@jhu.edu). Hamed Saeidi is with the Department of Computer Science, University of North Carolina Wilmington, Wilmington, NC 28403 USA (e-mail: saeidih@ uncw.edu). Simon Leonard is with the Department of Electrical and Computer En- gineering, Johns Hopkins University, Baltimore, MD 21211 USA (e-mail: sleonard@jhu.edu). Leila J. Mady is with the Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21287 USA (e-mail: lmady1@jh.edu). Martin J. Schnermann is with the Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702 USA (e-mail: martin.schnermann@nih.gov). Digital Object Identifier 10.1109/LRA.2023.3341773